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Our results also suggest that the modular organization of association cortex sub-networks may be more informative in predicting training-related gains than the modular organization of sensory-motor sub-networks. We have previously reported that SMART is associated with changes in functional connectivity of association cortex sub-networks, such as the default mode sub-network, and that these changes are associated with training-related cognitive gains [ 16]. This suggests that sub-networks that exhibit alterations with training may be more predictive of cognitive gains than those that do not exhibit training-related changes. Previous studies have also shown that individuals with greater segregation of association cortex modules have greater episodic memory performance [ 9]. In addition, association cortex modules, such as the default mode sub-network, reconfigure during working memory task performance [ 45– 47] and, importantly, these changes are related to higher task accuracy [ 45]. Finally, in normal aging, association cortex modules exhibit more pronounced changes in functional connectivity compared with sensory-motor modules [ 9], such that association cortex modules become less ‘segregated’, or modular, with advancing age. Thus, the modular organization of association cortex sub-networks may be particularly sensitive to the aging process and important in supporting complex behaviors. Depictions of within- (left) and between- (right) module connections for SMART subjects with low (top) and high (bottom) brain network modularity. The presence or absence of a connection was calculated for each connection density threshold (i.e., an adjacency matrix) for the top 2–10% of connections in 2% increments. For illustration purposes, we then averaged the adjacency matrices over thresholds for each subject, where edges represent the proportion of thresholds for which a connection was present between two regions (ranging from 0 to 1). Brain regions are colored according to their module assignments in Power et al. (2011) and are grouped into sensory-motor and association cortex modules as defined in Chan et al. (2014). The subject with high modularity has many connections within modules and fewer connections between modules compared to the subject with low modularity. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012;59: 2142–2154. pmid:22019881 Kellogg gets out of cookie business by selling Keebler, Famous Amos brands". WXIN. April 1, 2019 . Retrieved October 13, 2019.

Modular brain network organization is thought to support both specialized functions through communication within network modules and globally-integrated functions through communication between network modules [ 40]. Previous studies have provided support for the importance of this global network property by demonstrating that brain network modularity measured during a ‘resting-state’ is correlated with working memory capacity [ 41], predicts perception on a trial-by-trial basis [ 31], and is altered with varying task demands [ 42]. These studies suggest that modular network organization is related to both trait- and state-like aspects of cognition (e.g., working memory capacity and perceptual success, respectively). Here, we add to this previous work by showing that higher network modularity may represent an optimal brain organization for improving cognitive functioning with training. The benefits of highly modular networks have been previously demonstrated in both theoretical and empirical work. For example, computational models have shown that modular networks evolve in response to varying task goals and that this organization allows for rapid adaptation to new environments [ 43]. Further, individuals with higher general intelligence exhibit smaller changes in functional connectivity between a ‘resting-state’ and performance of a task, suggesting that high performing individuals have a more ‘optimal’ network organization at rest that supports more efficient changes in connectivity during task performance [ 44]. In the context of cognitive interventions, individuals with a more modular brain network organization may require less reconfiguration to achieve an ‘optimal’ state that allows for cognitive gains from training. Coyle, John J.; Bardi, Edward J.; Langley, C. John (1996). "15". The management of business logistics (6thed.). Minneapolis/St. Paul: West Pub. Co. ISBN 9780314065070. OCLC 33280849. Medaglia JD, Lynall ME, Bassett DS. Cognitive Network Neuroscience. Journal of Cognitive Neuroscience. 2015;27: 1471–1491. pmid:25803596 Aging is associated with declines in various cognitive functions, such as attention, cognitive control, and memory [ 1]. There is emerging evidence that characterization of large-scale brain network properties provides an important framework for understanding such complex behaviors [ 2, 3]. Previous work has shown that brain networks exhibit a modular organization, such that they are comprised of sub-networks, or modules. The extent of segregation of brain network modules can be quantified with a modularity metric [ 4], where highly modular networks have many connections within modules and fewer connections to other modules. Previous studies examining changes in modularity with aging have shown that older adults have less modular structural and functional brain networks than young adults [ 5– 8], particularly in sub-networks thought to mediate ‘associative’ functions, such as the fronto-parietal control and dorsal and ventral attention modules, compared to those involved in sensory-motor processing [ 9]. Bherer L. Cognitive plasticity in older adults: effects of cognitive training and physical exercise. Annals of the New York Academy of Sciences. 2015;1337: 1–6. pmid:25773610Stevens AA, Tappon SC, Garg A, Fair DA. Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity. PLoS ONE. 2012;7: e30468. pmid:22276205 Cohen J, Cohen P, West SG, Aiken LS. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Erlbaum, Mahwah, NJ; 2003. Schultz, Clark (July 29, 2019). "Kellogg closes on Keebler sale". Seeking Alpha . Retrieved August 12, 2019.

Among patients with knowledge deficits, the SMART program may facilitate informed decision‐making by helping them develop the skills needed to understand and use complex information concerning medication risks/benefits. Finally, as weaker network connections that do not pass our connection density thresholds may also be informative in predicting training outcomes, we quantified the ‘segregation’ [ 9] of each module from the Power et al. (2011) assignments, defined as: Subgroup analysis by primary and primary plus secondary prevention studies showed similar results to the main analysis. Meta-regression showed no apparent effect on results of mean follow-up time or study size. Sensitivity analyses excluding single studies showed no effect on the results. Fortunato S, Hric D. Community detection in networks: A user guide. Physics Reports. 2016;659: 1–44. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control. Journal of Neuroscience. 2007;27: 2349–2356. pmid:17329432Despite previous work showing that cognitive training can alter network connectivity in older adults, there has been little focus on identifying baseline neural factors that can predict training-related improvements in cognition. In a study with traumatic brain injury (TBI) patients, we found that brain network organization assessed at baseline predicted training-related cognitive gains. Specifically, individuals with higher baseline brain network modularity showed greater improvements on tests of executive functioning after goal-oriented attention self-regulation training [ 22]. These findings suggest that brain network modularity can be used as a biomarker to guide cognitive interventions as well as provide insight into the neural mechanisms underlying these interventions. However, the utility of brain network modularity as a predictor of training outcomes has not yet been tested in healthy individuals. In March 2001, The Keebler Company was acquired by the Kellogg Company. [1] At that time, headquarters were located in Elmhurst, Illinois. [23] Currently, Keebler has manufacturing plants in the United States, Thailand, Indonesia, and Malaysia. [ citation needed]